HEDGE EFFECTIVENESS OF THE RTS INDEX FUTURES
Anastasia Musorgina
A Thesis Submitted to the University of North Carolina in Partial Fulfillment of the Requirements for the Degree of Master of Business Administration
Cameron School of Business
University of North Carolina Wilmington
2011
Approved by
Advisory Committee
Nivine Richie Rob Burrus
Cetin Ciner Chair
Accepted by
______Dean, Graduate School
TABLE OF CONTENTS
ABSTRACT ...... iii
DEDICATION ...... iv
ACKNOWLEDGEMENTS ...... v
LIST OF TABLES ...... vi
LIST OF FIGURES ...... vii
INTRODUCTION ...... 1
Hedging ...... 1
Russian Derivatives Market ...... 2
Moscow Interbank Currency Exchange ...... 4
Russian Trading System ...... 5
Russian Trading System Index ...... 6
Russian Trading System Index Futures ...... 7
Effectiveness Measurement Methods ...... 8
LITERATURE REVIEW ...... 9
METHODOLOGY ...... 18
Sample ...... 18
Statistical Methods ...... 19
Measures ...... 20
Preliminary Tendencies ...... 24
RESULTS ...... 25
CONCLUSION ...... 27
BIBLIOGRAPHY ...... 29
TABLES ...... 31
FIGURES ...... 34
APPENDIX ...... 37
ii
ABSTRACT
This study focuses on the Russian derivatives market and the performance of the
Russian stock index futures.
With the improvement of legal regulation, the derivatives market in Russia has a real
growth potential. The undervaluation of the domestic enterprises and the high growth rates of
the Russian economy have led the stock market to the constant rise and growth, and the
Russian derivatives market to a considerable boost in activity during the last ten years.
However, as the rapid development of the Russian stock market cannot go on for ever, many private investors and professional managers feel the need of derivatives for investment hedging and reaping extra financial gains in the absence of the shares and bonds price surge.
The present paper analyses hedge effectiveness of the Russian Trading System Index
Futures from the first day of their trading on August 3, 2005 through July 29, 2011. The choice of the Russian market is justified on the grounds that it is relatively new and is among the leading global derivatives exchanges, which is actively developing.
To estimate hedge effectiveness the six year daily RTS Index and RTS Index Futures returns were divided into twelve equal semiannual periods. The coefficient of determination or the R-Squared was employed as the main measure of the effectiveness, calculated by an
Ordinary Least Squares regression analysis. In addition, a Minimum Variance Hedge Ratio model was applied to the hedge coefficient or the hedge ratio calculation. Besides, the
volatility of both variables was examined and compared to the hedging coefficient’s
fluctuations. The results showed that the RTS Index Futures contract fully satisfies the hedge
effectiveness terms and conditions, and strongly reflect the market behavior.
iii
DEDICATION
This thesis is dedicated to my parents to whom I owe my deepest and most heartfelt
gratitude, and who have made my studies at the University of North Carolina Wilmington possible. I genuinely appreciate their tender loving care and support over the whole study period and throughout my life. I am incredibly thankful for their empathy and patience and, also, for their gentle, warm and unfailing love that succor and encourage me.
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ACKNOWLEDGEMENTS
I express my immense gratitude to the University of North Carolina Wilmington, the
Cameron School of Business and the Graduate School. I am truly thankful to all the faculty members that teach International MBA courses at UNCW for their exceptional motivational ability and professional attitude. My special thanks go to Dr. Cetin Ciner for the offered valuable expert and considerate assistance. For the inspiration that he provided me with, facilitating to generate and explore interesting, constructive ideas.
I give my sincere thanks to Dr. William Sackley for his wholehearted concern and support, and also to Dr. Joseph Farinella for his understanding and kind attention.
Last but not least, I am very grateful to my committee Dr. Nivine Richie, Dr. Rob
Burrus and again Dr. Cetin Ciner for their guidance and keen interest
v
LIST OF TABLES
Table Page
1. Russian Trading System Total Trading Volume ...... 31
2. FORTS Trading Volume...... 31
3. FORTS and MICEX Shares in the Total Derivatives Trading Volume ...... 31
4. Descriptive Statistics for the Returns ...... 32
5. Hedge Effectiveness ...... 33
vi
LIST OF FIGURES
Figure Page
1. FORTS Instruments Trading Volume ...... 34
2. Hedging Coefficient Determination in Various Hedge Strategies ...... 34
3. RTS Index Futures Trading Volume, contracts ...... 35
4. RTSI Futures Returns Beta Coefficient ...... 35
5. RTS Index and RTS Index Futures Returns Volatility ...... 36
vii
INTRODUCTION
As a general rule, most people support the view that decision making under uncertain and risky circumstances is exceptionally difficult. In the case of financial and stock markets where the vast sums of money and even the future and the destiny of huge corporations are often at stake, this decision is twice as hard. The present paper entitled “Hedge Effectiveness of the RTS Index Futures” has its target audience of investors, executives, analysts together with finance, risk and portfolio managers. It is intended to give an account of Russian derivatives market and the extent to which eliminating risks with the most popular instrument in Russia RTS Index futures is successful.
The present chapter of the paper introduces the framework and objectives of the study.
This section also provides the interpretation of the hedging concept and the explanation for the Russian financial market preference. In addition, the fundamental characteristics and specific features of the hedging instrument are identified, as well as factors and indicators that influence and evaluate hedging performance.
Hedging To begin with, it is important to define the notion of hedging. Hedging is one of the most essential features of the derivatives market. It is also the most attractive and convenient way to neutralize systematic risks. The hedging principle is to fix the acceptable price level of the future transactions with financial instruments, such as forwards, futures or options. The major objective, however, is not to gain profit, but to preserve transactions from market volatility. This can be realized by taking an offsetting or reverse position. There are two forms of hedging in terms of the transaction type. Long hedge or buying should be employed if the risk is that prices or rates will go up. Oppositely, if the risk is that prices or rates will
decrease, selling or short hedge should be applied. As a rule, a company is long on assets and receivables, and short on liabilities and payables.
As previously mentioned, futures is one of the derivatives instruments that is used for hedging. A futures contract is a standardized agreement on the future delivery of the underlying asset of standardized quantity and quality for a price agreed today. Futures are traded on the exchange, with the exchange acting as an intermediary. An underlying asset to a futures contract can be commodities, currencies, securities, other financial instruments, interest rates or such intangible asset as a stock index which is used in the current study.
Russian Derivatives Market
This study focuses on the Russian derivatives market and the performance of the
Russian stock index futures. Such choice is justified on the grounds that Russian derivatives market is relatively new and there are not enough specialists in this area yet. Moreover, it is among the leading global derivatives exchanges and is actively developing.
Russian derivatives market has its origin in 1992. From the very beginning it has been highly volatile. That is why hedging was almost not employed. However, the necessity of hedging was proved by the economic crisis of 1998 that brought Russian derivatives market to its initial place. Thirteen years ago even banks, the largest players on the market did not commonly used hedging. Dramatic financial loses have initiated capital outflow, and the insolvency of an infinite number of banks including the major ones, reflected on the financial status of their clients. Eventually, it took more than a year in order to reach former results.
Still, Russian investors similarly to the investors in the developed countries are undoubtedly interested in minimizing their risks. This is vividly illustrated by the analysis of trading volume indices dynamics and the share of the derivatives market in the Russian stock market in general. In the first quarter of 2004 the trading volume was at the level of RUB
2
33.17 billion, in January 2006 it reached RUB 160.49 billion and in the middle of 2008 it was
already RUB 3,241.728 billion. On August 5, 2011 the total trading volume went beyond
RUB 445.592 billion (USD16.003 billion), or 8,762,666 contracts (rts.ru).
In the year 2007 the Government of the Russian Federation adopted quite a few
regulations that enabled the professional managers to use futures and options for hedging in
the individual asset management as well as in the investment and pension fund management.
Therefore with the improvement of legal regulation, the derivatives market in Russia
has a real growth potential. The direct evidence to this is the fact that during the last ten years
the constant rise and growth of the Russian stock market could have been observed, and the
Russian derivatives market has a considerable boost in activity. The securities’ market value
elevation is related to the undervaluation of the domestic enterprises, and also to the high
growth rates of the Russian economy. However, the market condition demonstrated that the
rapid development of the Russian stock market cannot go on for ever. That is why many
private investors and professional managers felt the need of derivatives for investment
hedging and reaping extra financial gains in the absence of the shares and bonds price surge.
Taking into consideration such a positive trend, it can be assumed that besides actively trading speculators and arbitrageurs, hedgers are becoming more customary to the market, and hedging positions from price risks is getting more important due to instability of particular domestic economic segments and crisis situation on the global markets.
A derivative trading is done through the derivatives exchanges as well as on the over-the-counter markets. Currently, on the Russian market the derivatives trading volume is far beyond stocks trading volume, which is clearly illustrated in the Table 1. This can be explained by the fact that derivative transactions are traditionally considered to be more profitable than those on the spot market. The underlying reason is not only the financial leverage but also the lack of transaction costs related to trading on the spot market, such as 3 margining expenses, depositary and settlement fees. Furthermore, the commission fees for transactions with derivatives are significantly lower than those on the spot market.
There are two derivatives markets in Russia, the Moscow Interbank Currency
Exchange or MICEX and FORTS (Futures & Options market on RTS) which is the part of the Russian Trading System (RTS) Exchange.
Moscow Interbank Currency Exchange
On the Russian derivatives market MICEX is a leading organizer of trading. The
MICEX floor holds a dominant position in the market for currency futures, with the share in this segment of about 95%. The Exchange organizes trading in US dollar futures and Euro futures as well as EUR/USD futures. In 2010, the total volume of trades in the MICEX
Derivatives Market Section has reached more than 2 million contracts. MICEX also organizes trading in futures on inter-bank money market interest rates (MosIBOR and
MosPrimeRate), which are calculated by the National Foreign Exchange Association (NFEA) and are acknowledged by the International Swaps and Derivatives Association (ISDA) as the reference ruble interest rate. In 2007, developing the derivatives market, the MICEX Group launched the MICEX Index futures trading. It became its first instrument in the stock derivatives segment. To ensure that obligations on futures trades are fulfilled, MICEX has a reliable risk management system. 190 organizations are members of the Derivatives Market
Section and 130 banks and financial companies trade in the derivatives market of the MICEX
Stock Exchange. To further develop the derivatives market, MICEX is making efforts to widen the range of traded instruments, first of all, by launching options trading. MICEX also intends to further increase the range of derivatives on stock assets, interest rate assets, and currency assets. (micex.com)
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Russian Trading System
The main and the leading derivative market in Russia and Eastern Europe is FORTS.
The trading started in September 2001. According to the Futures Industry Association the derivatives market of RTS is among the top ten global derivatives exchanges based on the results of the first quarter of 2011. This fact is supported by Table 2 that demonstrates the tremendous increase in the trading volume of futures and options on the Russian Trading
System from 2002 to 2011. Considering the total derivatives trading volume as 100%, the shares of the futures and options on the market are showed by Figure 1, which displays the popularity of futures oppositely to options.
With the development of FORTS, RTS pays close attention to risk management.
FORTS combines the developed infrastructure and reliability. The Russian Trading System
Stock Exchange guarantees high trading technologies conformed to more than ten years of successful market development. The essential part of any transaction in derivatives is the settlement of the contract on a certain date in the future under fixed conditions. Together with developing FORTS, Russian Trading System Stock Exchange pays special attention to
keeping its guarantee systems up-to-date. While improving its own system of guarantees, the
exchange constantly raises the demands to its clearing members. The FORTS participants are secure high-capitalized investment companies and banks. During the crisis of 2008-2009 the
Russian market fell by 80% with daily index fluctuations of 15% on selected days and volatility in individual stocks was even higher. RTS’s risk management system has successfully managed through such extraordinary environment. There was not a single case of any delay or non-payment.
Another significant task facing RTS is the development and implementation of a wide range of financial instruments that allows market participants to hedge price-related risks at
the stock market, foreign exchange market, debt and commodity markets. At present FORTS
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has the widest range of instruments in Russia. There are 54 contracts (40 futures and 14
options) on shares and bonds issued by the leading Russian companies, RTS Index, RTS
Standard Index, shares of Russian companies, short-term interest rates (average overnight
MosPrime rate and three-month MosPrime rate), currencies, and commodities including
Urals and Brent oil, gasoil, power, gold, silver, and sugar.
One of the distinctive features of FORTS is the ability of every participant to trade
using either their own terminals via Internet trading systems or workstations provided by
RTS. Market participants can efficiently transfer funds between the various RTS markets
including FORTS using the “single cash position” technology. (rts.ru)
Thereby, one of the main differences between the Moscow Interbank Currency
Exchange and the Russian Trading System is that there are bigger players on the MICEX.
However, this brings the RTS to the leading position in terms of trading volume. This is
described by Table 3 that provides the shares in the total trading volume of the two Russian
derivatives markets with regard to the number of trades and contracts for April 2010. That is
why for the purpose of this study the RTS Index futures have been chosen as the most
actively traded Index futures contract on the Russian derivatives market.
Russian Trading System Index
The RTS Index was first calculated on September 1, 1995. Ever since it become the
main benchmark for the Russian securities industry. RTSI is based on the Exchange’s 50
most liquid and capitalized shares. The list is reviewed every 3 months by the RTS
Information Committee. The Index value is calculated daily in US dollars in real time during the whole period of trading session on RTS.
6
Russian Trading System Index Futures
The RTS Index serves as an underlying value for the one of the most liquid
instruments on FORTS, RTS Index futures. Launched in June of 2005 it is the first stock
index futures contract in Russia. It is quoted as the RTS Index value multiplied by 100. For
instance, if the value of Index is 1952 points, then the Index futures will be quoted as 195200
points. In the first quarter of 2008 the RTSI futures trading volume gained 511% compared to
the similar period of 2007. Thus it became the fastest growing derivatives contract in the
world. In 2007 a professional magazine for the futures and options industries Swiss
Derivatives Review released an article on RTS Index futures saying that “in less than two
years of trading, the RTS Index futures contracts have become as accepted in Russia as the
leading blue chip stocks”1. In 2008 the US Futures Industry magazine has put the futures
contract on RTS Index in the 15th place in the trading volume rating (measured in contracts)
among other 20 stock derivative instruments most actively traded worldwide.
Futures contract on RTS Index provides market participants with a wide range of
opportunities to hedge stock portfolio risks and take advantage of movements of the entire
market and not just on the prices of certain stocks. These contracts are equally attractive to both small-scale investors and large market professionals. RTS Index futures provide
advantages over creating a portfolio of stocks linked to the RTS Index structure. They allow
RTS Index volatility trading and effective management of stock portfolios that don’t match
the RTS Index structure. Furthermore, using RTS Index futures it is possible to develop
different arbitrage and speculative strategies and create synthetic futures on the “second-tier”
stock index. Since buying and selling futures are symmetrical and equally simple
transactions, it enables to sell short the entire stock portfolio unlike short selling of the
1 Serdyukov, Y. (2007). RTS Index futures – the most popular instrument on the Russian market. Swiss Derivatives Review, 35, 16.
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underlying stock. Moreover, RTS Index futures contracts are cheap to work with. There are
no depositary fees and the financial leverage is free, as keeping positions is free of charge and
fees are charged only for a position opening and closing. Finally, as RTS Index is calculated in US dollars, it is very convenient to hedge portfolios that are in US dollars with index futures (rts.ru). The more detailed characteristics, specifications and various strategies that utilize RTS Index Futures are represented in the Appendix.
Effectiveness Measurement Methods
Considering the above-mentioned and described various features and peculiarities of the Russian derivatives market and index futures instrument, it is crucial to understand how this characteristics can be applied into a successful hedging strategy.
As a rule, the price changes of futures contracts are different from their underlying cash securities. Therefore, it is reasonable to evaluate possible hedge result beforehand. In this regard, to establish an effective hedging strategy, it is necessary to employ the proper hedge ratio or, in other words, to calculate the hedging coefficient. The hedge ratio represents the number of futures contracts held relative to the number of cash positions it covers. There are various methods for determining the hedge ratio. In this study the minimum variance hedge ratio (MVHR) is calculated, defining the investment portfolio in terms of two assets, namely a futures position and a cash position. In the case of this study the spot and futures historical daily prices from the RTS Index are used. One might think of the ratio as the slope coefficient or the beta of the portfolio with respect to the futures contract.
Another point is that the main objective of hedging is to reduce risk, so a rational investor will be interested in what share of the return dispersion of each stock is determined with the return dispersion of RTS Index futures. To find it out the indicator that describes the maximum risk reduction potential of a hedge can be used. The coefficient of determination or
8
R-squared of the ordinary least squares (OLS) regression is a popular measure of hedging effectiveness when the minimum variance hedge ratio is used. It is the coefficient of determination of futures price changes or the independent variable against cash price changes, the dependant variable. With strong correlations of the stock returns and index futures, the R-squared will be slightly lower than 1, meaning that the hedge will be effective.
However, if the variance is low, the R-squared will be low, implying that the hedge will not be successful.
Therefore, taking everything into account, the author of this study makes the hypothesis that hedging with the RTS Index futures is effective. In order to proceed with the research, the following structure outline of the paper is provided. The second section, the literature review, analyzes prior empirical and theoretical studies on the similar topic. The results and conclusions have been taken as a basis for the present research. The third chapter, methodology defines and tests the research hypothesis, and highlights the unique approach.
Also, the sample data is presented and described. Using statistical analysis, the relations are evaluated and hedging effectiveness is measured. Finally, the results of the study are interpreted in chapter 4 and conclusions along with the suggestions for the further research are provided in chapter 5.
LITERATURE REVIEW
With futures’ creation and introduction to the world financial market, a lot of research has been done; many approaches and methods have been developed and implemented to investigate instrument’s hedge effectiveness. Many models use spot and futures data and their price relations to find out and explain hedgers’ behavior.
The theory of hedging can be divided into the three main schools of thought, the traditional hedging theory, Working’s hypothesis and the portfolio theory. In 1979
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Ederington in his work entitled “The hedging performance of the new futures markets”
provided a brief description of these theories. He uses Government National Mortgage
Association Bills (GNMA) and 90 day Treasury Bills as the examples of the hedged items.
He asserts that the traditional theory highlights futures’ ability to minimize risk. Hedgers hold
positions that are opposite to spot, but that are of the same quantity. When the spot positions
are liquidated, the futures positions are closing simultaneously. The traditional theory
proposes that spot and futures prices usually tend to change together, that is why the
dispersion of the hedged position should be less than the unhedged position’s dispersion. This
issue is often explored within the context of the spot price and the basis changes comparison.
The basis is defined as the difference between the futures and spot prices. The hedge is
considered to be perfect if the basis changes equal zero. It is commonly stated that the value
and the change of the basis are rather small, as there is an opportunity to make and accept the
delivery of the futures contract’s underlying asset.2
Criticizing the traditional theory, Ederington singles out the spot and futures prices
ability to change differently. For example, he says “the theory of adaptive expectations
implies that if futures prices reflect market expectations they should not normally match changes in cash prices.”3 He also stresses that Working has pointed out this fact as well. “A
major source of mistaken notions of hedging is the conventional practice of illustrating
hedging with a hypothetical example in which the price of the future bought or sold as a
hedge is supposed to rise or fall by the same amount that the spot price rises or falls.”4
2 Ederington, L. H. (1979). The Hedging Performance of the New Futures Markets. The Journal of Finance, 34(1), 159-160.
3 Ibid, 160.
4 Ibid.
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The opponent of Keynes’s backwardation theory of futures prices Holbrook Working
in his studies questions the risk minimizing goal of hedging. Instead he focuses on the profit
maximizing hedging aspect. In other words, he believes that hedgers act more like
speculators. Because they hold both, futures and spot positions, the relative change between
the prices on the spot and futures markets are of the greater interest to them than the absolute
change value. That is why, according to Working “most hedging is done in expectation of a
change in spot-futures price relations”.5 In his view those who hold long positions will hedge
if the basis is expected to fall and will not hedge if expected otherwise.
Examining the portfolio approach, Ederington follows Johnson and Stein who have
examined hedging in terms of the portfolio theory fundamentals’ application. This enabled
them to unite Working’s profit maximization hypothesis with the risk avoidance of the
traditional theory. Johnson and Stein state that futures, similarly to the other assets, are
bought and sold for the risk-return reasons.
Ederington notes that the traditional theory points out that hedgers will always hold
hedged positions, whereas the Working’s hypothesis clarifies that hedgers will hold either
fully hedged or fully unhedged positions. Thus, employing the portfolio theory allows
Johnson and Stein to explain why hedgers will hold fully hedged as well as fully unhedged
positions simultaneously.
Applying the portfolio theory to hedging has one peculiarity in comparison with the
common portfolio approach. This is that the spot and futures markets are not regarded as
being interchangeable. The spot position is considered to be fixed, and the decision is made
upon which of its part should be hedged. So, as opposed to the traditional theory, the
portfolio approach assumes that the spot position can be fully or partially hedged.
5 Ederington, L. H. (1979). The Hedging Performance of the New Futures Markets. The Journal of Finance, 34(1), 160.
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The decision making under the portfolio theory is graphically described by Figure 2.
Within the portfolio theory framework an investor aspires to maximize the expected return. In this connection, among the various portfolio options that are portrayed with the ABCD curve,
an investor should choose a portfolio that is in the “risk-return” point of the highest plotted
indifference curve. Such portfolio is represented by the point B, with the hedging coefficient
of zero.
Hence, Ederington claims that the hedging decision on the spot or forward positions
on the futures market does not differ from any other investment decision. Investors hedge to
receive the best risk-return combination. In order to develop the measurement of hedging performance and estimate the potential of futures markets to reduce risk, Ederington considers that risk reduction depends only on the part of the hedged spot position. He compares the risk of an unhedged portfolio with the minimum risk of a portfolio that contains spot and forward securities. Also, he views interest as an irrelevant aspect of the hedging
decision. He found out that this minimum risk matches the variance of the portfolio return
where the part of the hedged spot position equals the risk minimizing part of the hedged spot position. Therefore, the measure of hedging effectiveness which Ederington uses in the study is the percent reduction in variability. Furthermore, he employed an ordinary least squares
(OLS) model, and considered the hedge to be effective when the R-squared or the coefficient of determination of the regression was rather high. To put it another way, when high percent of the variation in one variable was explained by variation in another variable. What is more,
Ederington developed and successfully applied what is known today as the minimum variance hedge ratio (MVHR).
Cecchetti, Cumby and Figlewski (1988) in the study named “Estimation of the optimal futures hedge” provide an example of the U.S. Treasury bond hedging coefficient determination with T-bond futures on the basis of the expected utility maximization. As the
12
function of the expected utility the authors use the logarithmic function. The authors state that
risk minimization cannot be viewed as an optimal hedge without its impact on the expected
return. “In equilibrium, the risky securities are priced to earn an expected premium over the riskless rate.”6 Eliminating risk via hedging should also eliminate the expected return to
bearing that risk. “This is the cost of hedging, and the reward to risk bearing in the futures
market.”7 Only an investor that does not want to take risk can make an optimal hedging decision without considering its effect on the risk and return simultaneously. An optimal futures hedge is the hedge that maximizes the expected utility.
As follows from Figure 2, and as noted by Cecchetti, Cumby and Figlewski (1988)
“… the optimal hedge ratio is a function of both the risk-return possibility curve that is available in the market and the investor’s utility function. Implementation of this hedging strategy involves two steps: first, estimation of the joint distribution of returns, and then
optimization, by finding the hedge ratio that maximizes the investor’s expected utility.”8
Among the numerous studies there are some that focus specifically on hedge effectiveness of index futures. One of the first studies in this field was conducted and described by Figlewski. In 1984 he explored the effectiveness of the US S&P 500 stock index futures contracts. For stock index futures basis risk which is associated with imperfect hedging and that equals spot price of a hedged asset minus futures price of a contract, is a more serious problem, compared to Treasury bills and bonds. As the firm-specific component of the return on the cash stock position is the clearest cause for the basis risk and since index futures are tightly bounded to the performance of an underlying stock market index, nonmarket or unsystematic risk cannot be hedged.
6 Cecchetti, S. G., Cumby, R. E. and Figlewski, S. (1988). Estimation of the Optimal Futures Hedge. Review of Economics and Statistics, 7, 625.
7 Ibid.
8 Ibid. 13
Like Ederington, Figlewski also excludes dividends from the returns, considering dividend risk to be not an important factor. The risk minimizing hedge ratio that he uses is
also the minimum variance hedge ratio (MVHR). The results of the study show that it is the
best for such analysis. Figlewski established that risk can be entirely eliminated by hedging
only with perfect correlation. What is more, using the US stock data, he proved that one week
hedges performed better than overnight hedges but that little or even no positive effect at all
was revealed by prolonging the hedge period to 4 weeks. It was also found that the futures’
timing of the expiration is not a significant component. Figlewski came to the conclusion that
risk reduction can be achieved by approximately 20% to 30% of the unhedged portfolio’s
standard deviation and that the basis risk increases as the duration of the hedging horizon
decreases.
Finally, most recent papers use liner regression analysis as well. According to
Kawaller (2002) employing linear regression analysis to measure effectiveness requires
thorough knowledge and understanding of statistical components. He draws conclusions
about his study considering the Statement of Financial Accounting Standards No. 133,
Accounting for Derivative Instruments and Hedging Activities (SFAS 133). Kawaller
proposes that the major measurement of hedging performance is the coefficient of
determination or the R-squared. He suggests that as the R-squared is determined by squaring
the correlation coefficient, the possible range of the R-squared statistic is from zero to one.
However, to be particularly effective R-squared should have the value of at least 0.80.
Another suggestion is that linear regression should be used as a prospective test and the dollar
offset method retrospectively for several reasons. Firstly, the dollar offset method is simple
and easy to calculate, though it is hard to achieve high effectiveness from period to period.
Secondly, the Derivative Implementation Group (DIG) Issue E7, Methodology to Assess
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Effectiveness of Fair Value and Cash Flow Hedges, allows a company to continue hedge if the retrospective test fails as long as the new prospective test meets 80% effectiveness.
In 2002 Finnerty and Grant worked on the accuracy of the regression methods, attempting to develop and improve them. They found out that for an effective regression besides the R-squared of 80%, the intercepts should be close to zero and slope coefficients close to 1. To estimate the hedging effectiveness measurement, Finnerty and Grant substituted the three components of the regression efficiency into a prospective Regression
Variability-Reduction method formula, RVR, which they considered to be more reliable than
R-squared. The RVR is suggested to be a more accurate measure of effectiveness because it accounts for changes in the intercept coefficient and the regression slope coefficient. Also, this formula easily allows retrospective testing by simply replacing the estimated slope coefficient with actual hedge ratio used.
In practice and theory not only the absolute values of futures and spot prices changes are used in order to compute hedging coefficients, the percentage change and the logarithmic changes are as well utilized. In the article called “Minimum-variance futures hedging under alternative return specifications” Terry (2005) describes the company that is long one unit of a particular asset, and is hedging this position using a specified futures contract. The author’s objective is to find the hedging coefficients for the situations when the payoff variance, the return variance and the hedged position returns’ variance are minimized. Moreover, he wonders whether the values of the coefficients that comprise different variables are the same or not.
He derives the hedging coefficients of minimal dispersion for the three cases in which returns are measured as the absolute changes of spot and futures prices or in percentage terms, as the simple returns or in dollar terms, as the continuously compounded returns of the spot instrument and futures contract or in log terms. Analyzing the first two situations, the
15
author notes that the results are not dependent on any specific form for the joint spot and
futures return process. There were no assumptions made about this process except that they
have well-defined dispersion and mathematical expectation or average of distribution.
Based on the results of the study, Terry comes to the following conclusions. Firstly, if the absolute changes of the spot and futures prices are used in the calculations, the coefficients minimize the dispersion of the hedger’s position. Secondly, if the simple or continuously compounded returns are utilized, the coefficients’ values have errors. The difference between the three cases is subtle if hedging is done for the nearest maturity futures contract. Terry notes that the articles on the same research problem reviewed the equivalent situations. So, it is evident that the authors achieved favorable results for the three alternatives.
The hedging coefficients that are calculated with the simple and logarithmic returns for cross-hedging can markedly abate the hedging results. Additionally, Terry highlights that the hedging coefficients can be computed with each of the above defined variables. The selection of the variable depends upon the features of its statistical array. For instance, the majority of the market participants prefer to work not with the absolute values but with percent changes.
The outcome of the three cases shows that they provide the same results, i.e. the
hedging coefficients’ values do not depend upon the different target setting for the variance
minimization. Moreover, it follows that the minimum variance hedge ratio (MVHR) is
approximately equal to the results of the three cases.
The current study investigates hedging performance of the RTS index futures. In this
regard, Russian financial literature and Internet academic sources have been reviewed. The
research showed that very few similar studies have been conducted for the Russian financial
market. The reason for the lack of such analysis is the relative novelty of futures contracts 16 and Russian derivative market in general. In addition, since derivatives appeared in other countries before they became commonly used in Russia, Russian financial specialists adopt and apply rich foreign experience and knowledge.
The first and one of the most experienced authors who undertakes research and investigates futures hedging on the Russian derivatives market is Burenin. Burenin (2009) in his book “Hedging with the RTS futures contracts” accurately and in detail describes various hedging strategies and provides examples that are based on historical data of the Russian
Trading System. In addition, he gives an account of the hedging spot positions of stocks of the certain Russian companies, investment portfolios and the United States dollar with the
RTS futures. The author carefully analyzes the issues of stock portfolio hedging particularly with the RTS Index Futures. For example purposes Burenin uses the portfolio of the six most significant companies that are included in the RTS Index, which are Gazprom – energy company, Lukoil – oil and gas company, Norilsk Nickel – nonferrous metal producer,
Rosneft – petroleum company, Rostelecom – telecommunications, Sberbank – banking. First of all, Burenin calculates the theoretical hedging coefficient relative to the RTS Index and
RTS Index Futures. He states that employing stock betas, which are based on the RTS Index
Futures, improves hedging results compared to utilizing the RTS Index betas. The hedging coefficient Secondly, as the main measures of hedge effectiveness the author estimates the coefficient of determination and the unhedged portfolio risk value. The author uses daily prices of the six companies from July 2, 2008 till August 12, 2008, and September 2008 RTS
Index Futures. Burenin finds out that the beta coefficient equals 0.89. Moreover, 47.68% of the portfolio returns’ variation is explained by the RTS Index Futures contract returns’ variation. Correspondingly, 52.32% of the portfolio returns’ variance does not depend upon the futures contracts’ returns, which brings in risk to the hedger’s position.
17
Therefore, summarizing all the above reviewed and described various literature sources, it is crucial to notice that as Cecchetti, Cumby and Figlewski (1988) pointed out “the return on a hedged position will normally be exposed to risk caused by unanticipated changes in the relative price between the position being hedged and the futures contract. Because of this “basis risk”, no hedge ratio can completely eliminate risk.”9 Besides, the hedge
coefficient calculations are performed on the basis of historical data. That is why such data
cannot aptly characterize the future time period of the hedging objective.
METHODOLOGY
Taking into consideration the key finding from the previous theoretical and empirical
studies, the preliminary analysis for the present research can be carried out.
Sample
It is necessary to identify the research data to be going on with. The sample data
consists of historical end-of-day RTS Index and RTS Index Futures prices. The information is
taken from the RTS web-site. For the starting date August 3, 2005 is taken as the first day of
the RTS Index Futures trading. The end date is July 29, 2011. The closing prices for the futures were used for the nearest maturity contract. Besides, the prices for the both variables were afterwards applied for the returns (R) calculation as the log price differences between the daily closes, using the following formula R = ln(pt) - ln(pt-1). Continuously compounded
RTS Index returns are calculated as RS = ln(St/S0), and the returns of the RTS Index Futures
(RF) equal ln(Ft/F0), where St and Ft are the spot and futures prices, respectively, at period t.
S0 and F0 are initial spot and futures prices.
9 Cecchetti, S. G., Cumby, R. E. and Figlewski, S. (1988). Estimation of the Optimal Futures Hedge. Review of Economics and Statistics, 7, 624. 18
The data also includes the RTSI Futures trading volume, in order to receive the
statistical evidence that the contract has been of the strong and increasing interest since its
appearance on the market and to forecast or estimate the change in the main indicators of
successful hedging.
The summary data of the RTS Index Futures trading volume, computed in contracts,
is conveyed in Figure 3. It provides the exponential smoothed trend line that illustrates the
instrument’s tremendously increasing popularity.
The data shows that the number of contracts has flied up from significantly less than
one hundred to more than 3 million in six years. Such buoyant demand for the contract and
the huge upsurge of its trading volume have a substantial beneficial influence on the major
indicators of the hedge effectiveness, allowing to make a prediction for their gradual and
positive changes that will be described later in this section.
Statistical Methods
To test the hypothesis that hedging risks with the RTS Index futures is effective, the
ordinary least squares (OLS) regression model is used. In other words, the regression
examines the causation between the Index and the Index Futures returns and the influence of
Futures on the Index, and it also helps to quantify the magnitude of a relationship between
two variables. It is calculated with the following formula: Y = α + βX, where Y is the dependent variable or spot positions, X is the independent variable or Index Futures, α is the intercept or the value of Y when X is equal to zero, β is the slope of the line or the slope coefficient, which measures the change in Y with respect to X. However, the beta coefficient of the OLS regression is not always constant, therefore running only one regression for the whole sample is not sufficient. That is why the examined six year period is divided into twelve separate but equal semiannual periods. Thus, to indicate the successful hedge in the
19 present study, the twelve intercepts (α) should equal zero and slope coefficients (β) should be approximately 1.
Measures
One might think of the slope coefficient or the beta of the portfolio with respect to the futures contract as the hedging coefficient or the hedge ratio. According to Cecchetti, Cumby and Figlewski (1988) there are many models of the ratio and risk-return strategies, which are illustrated by Figure 2. The vertical axis corresponds to the expected return and the horizontal axis is the standard deviation. The hyperbola (ABCD curve) represents the “risk-return frontier” of the portfolio which comprises the hedged asset and futures contracts. The present model does not include riskless asset, such as a government bond, because it is also a hedged item. Figure 2 depicts four lines ascending from left to right. They are the indifference curves, which characterize investor’s expected utility function. The higher is the indifference curve, the greater utility level it corresponds with. At the point A investor’s spot position is not hedged, that is why the hedging coefficient h equals zero. The “risk-return” point of the highest plotted indifference curve represents the portfolio at the point B, with the hedging coefficient of zero. The point C is the portfolio with the minimum risk, therefore the hedging coefficient h* is the coefficient of the minimal dispersion. At the point D the futures contracts number equals the quantity of the spot asset as h=1. This situation is typical for the traditional theory.
As described by Figure 2, there are various hedging strategies that lead to different hedge ratios. However, this study employs the minimum variance hedge ratio (MVHR), defining the investment portfolio in terms of two assets, futures and cash positions. In order to devise the MVHR’s formula, first it is necessary to find out the expected return of the whole hedged portfolio. Following Ederington (1979) the expected single period gross returns E(U), earned on the RTS Index holding (XS) are defined as E(U) = Xs {E(S2)- S1}, 20
where E(S2) is the spot price expected after one period and S1 is the current spot price. The
2 2 2 variance of the unhedged position is then simply Var(U)=XS σS , where σS is the variance of
the change in the spot price over time. The expected return of a hedged portfolio (R) which
includes both the RTS Index spot position (XS) and RTS Index Futures holding (Xf) can be
expressed through the following formula:
E (R) = Xs {E(S2)- S1} + Xf {E(F2) – F1} – K (Xf),
where E(F2) is the expected futures price one period hence and F1 is the current futures
contract price. K (Xf) is the brokerage and other costs of futures transactions.
The variance of the portfolio return is then determined as
2 2 2 2 Var (R) = XS σS + Xf σf + 2XSXf σSf,
where σS, σf , σSf represent the subjective variances and the covariance of the possible price
changes from time 1 to time 2. It is important to note that a portfolio can be hedged
completely or partially, in other words futures market holdings may not equal negative spot
market holdings. Indeed, they both can have the same sign. That is why, the part of the
hedged spot position is b = –Xf/XS. Since in a hedge XS and Xf have opposite signs, b is
usually positive. Therefore, the variance of the portfolio returns is
2 2 2 2 Var (R) = XS (σS + b σf - 2b σSf)
By setting the partial derivative of Var(R) with respect to b to zero, with the assumption that
XS is exogenously determined or fixed, the resulting expression for the risk minimizing part
of the hedged spot position (b*) is as follows:
2 b* = σsf /σf = ρ (σs /σf) = Cov(Rs,Rf)/Var(Rf),
where ρ is the correlation coefficient between the RTS Index Futures prices (Rf) and the RTS
Index spot prices (Rs). σs, σf are the standard deviations of Rs and Rf respectively. To put it
21 another way, the hedge ratio compares the prices of the RTS Index futures to the prices of the
RTS Index spot positions it covers.
According to Cecchetti, Cumby and Figlewski (1988), and taking into account Figure
2, it can be deduced that if the futures price is characterized by the same or higher volatility than of the spot price, which is a typical case, the hedging coefficient of the minimum dispersion cannot be greater than their correlation coefficient that will be less than one.
Therefore, the hedging coefficient of the minimal dispersion is usually less than one.
Moreover, as it is reasonable to evaluate possible hedge effectiveness beforehand and to estimate the potential for risk reduction in the price changes of the RTS Index spot positions and the RTS Index futures, the coefficient of determination or the R-squared of the ordinary least squares (OLS) regression is used. The R-squared determines the strength of the correlation of a dependent variable, the spot positions and an independent variable, the index futures. To put it another way, it shows a rational investor the percentage of the RTS Index
Futures performance that is explainable by the performance of the benchmark RTS Index.
Also, the R-squared is used as a measure of how reliable, predictable, and valid the alpha and the beta are, or of how well the regression line represents the data.
The coefficient of determination of a variable equals the square of its correlations with another variable. In the case of this study, one variable is the RTS Index returns and another is the returns of RTS Index futures. The correlation coefficient can be determined as β
(σF/σS), where β is the market Index hedge coefficient, σF is the standard deviation of RTS
Index futures return and σS is the standard deviation of the spot returns.
The R-squared is always a positive number, which may vary from zero to one. If the regression line passes exactly through every point on the scatter plot, it would be able to explain all of the variation, signifying the perfect correlation. The coefficient of determination would therefore equal 1. Though, in practice, this special case of the R-squared 22
that equals one is not possible over any but the shortest time interval. On the contrary, the
further the line is away from the points, the less it is able to explain the variation. The R- squared will then be low, implying that the hedge would not be successful.
Generally, the R-squared greater or equal 0.80 is accepted as an effective hedge indicator. Consequently, supposing that hedging with the RTS Index futures is effective, the
R-squared should be slightly lower than 1, also the R-squared will reflect the growth in the
RTSI Futures trading volume in its value lift.
To investigate even further, the volatility changes of the RTSI and the RTSI Futures
contract are measured. Prices and returns volatility tell investors about the “risk” associated
with their investments. Measuring and forecasting volatility of financial asset returns is
important for portfolio and risk management. Markowitz (1952) and Tobin (1958) were first
who measured risk by means of the statistical variance and standard deviation in the value of
a portfolio. Markowitz introduced Modern Portfolio Theory, in which he suggested that
alternative investments should be evaluated in terms of both expected return and associated
risk. Investors can obtain greater return by bearing greater risk, or reduce the risk of the
portfolio by means of giving up expected return. The main measure that he employed is the
variance that is the determinant of the average of the squared deviation of the actual return
from its expected value. Tobin (1958) developed Markowitz's work by adding a risk-free asset to the Portfolio analysis theory. In his paper, he states that in order to assess the risk of
the portfolio, the standard deviation should be used. Standard deviation is calculated as the
square root of the variance.
At present, standard deviation is the most common measure of volatility in portfolio
analysis. Hypothetically, standard deviation is an efficient and unbiased estimator for volatility. Though in fact “returns at very high frequencies are distorted such that the realized
23
variance could become inconsistent”.10 In this study the volatility is basically the standard
∑ deviation of each of the variables, which is calculated as S = . In this
formula, is the value of the mean, N is the sample size, and represents each data value
from i=1 to i=N. Again, using semiannual sub-periods of the historical prices data, the values
can be calculated and the trend can be observed. Additionally, to reckon with the RTSI
Futures trading volume escalation, the volatility change is supposed to reveal a declining tendency.
Preliminary Tendencies All things considered, the author of this study regard the R-squared greater or equal
80%, the intercept equal 0, the slope coefficient close to 1 and decreasing volatility changes, as the main indicators of an effective hedge. Moreover, the OLS regression, which is calculated for several semiannual periods, distinguishes this study from many others. Also, this approach demonstrates how the R-squared and the beta coefficient change through time.
The R-squared and the beta fluctuations throughout the six-year period will most likely be affected by the global financial crisis of 2008. The economic crisis exhibited the lag of the main economic indicators in almost every country. Its direct catalyst was the United
States subprime mortgage crisis. Rather fast the reliable borrowers experienced troubles with getting a bank loan. The breakdown progressively transformed from the mortgage juncture to the financial crisis, and started to affect not only the United States. By the beginning of the year 2008 the crisis expanded to a global scope, and became gradually conspicuous in the production cutback, damping of the raw materials prices and demand, and growth of unemployment.
10 Martens, M. and Van Dijk, D. (2007). Measuring volatility with the realized range. Journal of Econometrics , 138, 182. 24
Moreover, in view that RTSI Futures’ trading volume has been growing over time, the
R-squared values will demonstrate the rise over the twelve semiannual periods of the
regression. Besides, running only one regression for the whole sample means assuming that
the beta coefficient is constant, which may not be true.
RESULTS
The descriptive statistics on the RTS Index and RTS Index Futures returns for the
whole research period is represented in Table 4. The descriptive statistics analysis describes
the data in terms of central tendency, variability and distribution normality. One of the most
pertinent values for the current research is the simple measure of the central tendency that is
the mean or an arithmetic average. As the sample data comprises rather low figures, the mean
is small as well and equals 0.00062 and 0.00060 for the RTSI and RTSI Futures returns
respectively. One of the variability measures reported in the descriptive statistics is the
standard deviation that shows the spread of the values around the central tendency, which are
0.025 and 0.031 for RTSI and RTSIF respectively. Concerning, the distribution normality
indicators, the skewness measures the asymmetry of the probability distribution. In other
words, the historical pattern of returns does not resemble a normal, i.e. bell-curve
distribution. In the present research both variables have the negative skewness, so this
distribution has a “tail” which is pulled in the negative direction. This means that there are
fewer values that are less than the mean, and they are farther from the mean than are higher
values. The peakedness of the data is measured and illustrated by the kurtosis figure.
Noticeably, both variables have very high kurtosis values, which demonstrate that the
exceptional values occur more frequently than in a normal distribution. These values form the
“fat tails” that suggest a higher percentage of very low and very high returns than would be expected with a normal distribution.
25
The hedging effectiveness for the six-year research period as well as for the twelve
semiannual sub-periods is outlined in Table 5. It is measured with the coefficient of determination or R-squared, the Minimum Variance Hedge Ratio, the alpha or the intercept of the regression, the t-statistics and the P-value. Regressing the twelve sub-periods produced the R-squared increasing values from 0.82175 to 0.96116, signifying that more than 82% of the variation in the RTS Index spot returns is explained by the variations in the RTS Index
Futures returns. The R-squared figure for the whole six-year period is 0.87841, which means that more than 87% of the deviation intercept is described by this model. These figures are rather high and show that the model is stable.
The beta or the slope coefficient values for the whole research period and all the sub-
periods vary from 0.68 to 0.90, representing that the RTS Index Futures bear less systematic
risk than the market. Besides, it estimates how the spot price will change on average with the futures price changes. Positive beta values show that the Futures and Index returns change in the same direction. That is to say that with the market Index returns increase or decrease by
1%, the RTS Index Futures will respectively grow or drop by the value of its beta coefficient, which is less than 1%.
Furthermore, the alphas or the intercept coefficients are approximately equal to zero, which is also a positive sign of the RTSI Futures efficient hedge ability.
The t-statistics for the RTSI Futures of the same regressions have appeared to be extremely high, the values range from 23.42 to 55.17, and the P-value is less than 0.01 and is almost zero. Therefore, a conclusion can be made that this variable is significant at 1% level, and the null or void hypothesis is rejected with 99% confidence. Thus, supported by rejecting the null hypothesis, the research hypothesis that the RTSI Futures are hedge effective is accepted. The P-values for the intercept are more than 0.1, which is a rather high number, and
26
the t-statistics are low. Therefore, the null hypothesis is accepted, which means that there is
no evidence of the statistically significant relationship between the two variables.
It should be noted that the hedge effectiveness measures have considerably weakened
in 2008. What is more, the beta coefficient fluctuations, portrayed in Figure 4, and the
volatility analysis of the RTSI and RTSI Futures returns, which is conveyed in Figure 5,
reveal that the two variables have become fairly riskier during this period. This situation is
easily explained by the global financial crisis and reflects the depressive condition of the
markets at that time.
Hence, all the values fully satisfy the hedge effectiveness terms and conditions that
were laid down in the methodology section, implying that hedging with the RTS Index
Futures is effective.
CONCLUSION
Today, hedging plays an important role in the global economic stability. Though, in
Russia hedging is still an emerging field, in the developed countries it has become common
long time ago. After Russian economic crisis of 1998 the fact that the unhedged risks lead to losses has appeared to be self-evident. Those who do not hedge will lose if the same risks are hedged by the competitors. None the less, the hedging decision must have a solid basis and be thoroughly thought out. A lot of factors have to be taken into account, and the hedge effectiveness is one of them. This subject is sufficiently challenging, as its aim is not only additional profit earning, which is undoubtedly its supplementary benefit if the hedge is successful, but to make the operations more efficient considering risk minimization at a defined rate of the return.
27
Using Russian Trading System Index and RTS Index Futures returns it has been
hypothesized that the RTS Index Futures are hedge effective. Efficiency was measured for the six-year period from August 3, 2005 until July 29, 2011. As the criteria for the effectiveness
the R-squared, the t-statistics, as well as the slope and the intercept coefficients were chosen.
This study provides the effectiveness levels for the twelve semiannual intervals during the whole research period. All the obtained figures demonstrate the perfect ability of the RTS
Index Futures to efficiently hedge. Moreover, the volatility analysis and the observation of the beta coefficients’ fluctuations convey that the RTS Index Futures strongly reflect the market behavior.
28
BIBLIOGRAPHY
1. Burenin, A. (2009). Hedging with the RTS futures contracts. (1st Edition). Moscow: NTO Vavilov.
2. Cecchetti, S. G., Cumby, R. E. and Figlewski, S. (1988). Estimation of the Optimal Futures Hedge. Review of Economics and Statistics, 7, 623-630.
3. Ederington, L. H. (1979). The Hedging Performance of the New Futures Markets. The Journal of Finance, 34(1), 157-169.
4. Figlewski, S. (1983). Hedging Performance and Basis Risk in Stock Index Futures. The Journal of Finance, 39(3), 657-669.
5. Finnerty, J. D. and Grant, D. (2002). Alternative Approaches to Testing Hedge Effectiveness under SFAS No. 133. Accounting Horizons, 16, 95‐108.
6. Kawaller, I.G. (2002). Hedge Effectiveness Testing Using Regression Analysis.
7. Kotzé, A. A. (2005). Stock Price Volatility: a Primer. The South African Financial Markets Journal, 1.
8. Lence, S. H. (1995). The Economic Value of Minimum-Variance Hedges. American Journal of Agricultural Economics Association, 77, 353-364.
9. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance , 7 (1), 77-91.
10. Martens, M. and Van Dijk, D. (2007). Measuring volatility with the realized range. Journal of Econometrics , 138, 181–207.
11. MICEX. (n.d.). MICEX Group Profile, available at http://www.micex.com/group/profile/portrait
12. Mohr, N. G. and Smit, E. M. (1995). Minimum variance hedge ratio analysis for the South African share index futures market: duration and expiration effects. Investment Analysts Journal, 40, 9-25.
13. Serdyukov, Y. (2007). RTS Index futures – the most popular instrument on the Russian market. Swiss Derivatives Review, 35, 16-17.
14. RTS. (n.d.). Futures Contract on the RTS Index, available at http://www.rts.ru/a19902
15. RTS. (2011, August). RTS Index futures trades over USD8 billion, available at http://www.rts.ru/a22709/?nt=12
16. RTS. (2011, August). The total trading volume on FORTS surpasses a USD16 billion mark, available at http://www.rts.ru/a22725/?nt=120
17. RTS. (2008). The Year in Review, available at http://www.rts.ru/a18260/?nt=108
18. Terry, E. (2005). Minimum-variance futures hedging under alternative return specifications. The Journal of Futures Markets, 25(6), 537-552.
19. Tobin, J. (1958). Liquidity Preference as Behavior Towards Risk. The Review of Economic Studies, 25 (2), 65-86.
20. Working H. (1953). Futures Trading and Hedging. The American Economic Review, 43(3), 314-343.
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TABLES
Table 1: Russian Trading System Total Trading Volume
RTS Trading Volume in July 2011, USD RTS Trading Volume in July 2011, % Derivatives 154 415 033 772.00 93 Stocks 10 805 463 046.00 7 Total 165 220 496 818.00 100
Table 2: FORTS Trading Volume
Trading Volume, Trading Volume, RUR Number of Trades Contracts
2 Sept 2 Sept 2 Sept 2 Sept 2 Sept 2002 2 Sept 2011 2002 2011 2002 2011
Futures 247 079 733.00 192 627 210 269.00 75 838 3 852 146 1 950 820 510
Options 3 170 000.00 14 586 855 829.00 984 194 703 174 15 255
Total 250 249 733.00 207 214 066 098.00 76 822 4 046 849 2 124 835 765
Table 3: FORTS and MICEX Shares in the Total Derivatives Trading Volume
Trading Volume, Trading Volume, Number of Number of Trades, Contracts* % Trades* % FORTS 43 754 389 96 8 699 104 95 MICEX 2 022 691 4 497 358 5 Total 45 777 080 100 9 196 462 100 * The data for April 2010
Table 4: Descriptive Statistics for the Returns
RTSI Returns RTSIF Returns
Mean 0.000621387 0.000603566 Standard Error 0.00064433 0.000805455 Median 0.002352467 0.001212712 Mode N/A -0.005249356 Standard Deviation 0.02482972 0.031038781 Sample Variance 0.000616515 0.000963406 Kurtosis 11.45448141 23.17944571 Skewness -0.458176861 -0.424538088 Range 0.414033424 0.62807296 Minimum -0.211994243 -0.275742852 Maximum 0.202039182 0.352330109 Sum 0.92275938 0.896295128 Count 1485 1485 Confidence Level (99,0%) 0.001661822 0.002077387
32
Table 5: Hedge Effectiveness
H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 6 YRS 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 The Intercept (α) Coefficient 0.00104 0.00002 0.00032 0.00015 -0.00010 0.00013 -0.00348 0.00099 0.00051 0.00005 0.00027 0.00007 0.00017 t-Statistics 1.63004 0.02512 0.74205 0.35502 -0.21553 0.27704 -1.72517 1.31578 0.97227 0.13914 1.09408 0.19769 0.75119 P-value 0.10574 0.98000 0.45947 0.72319 0.82972 0.78222 0.08709 0.19069 0.33286 0.88957 0.27609 0.84362 0.45266
The Slope (β) Coefficient 0.84885 0.75779 0.76309 0.78718 0.89699 0.85665 0.68023 0.82527 0.79919 0.86401 0.88119 0.78691 0.74975 t-Statistics 23.42266 28.79990 30.64903 31.26140 33.16629 31.92424 26.40523 43.39734 39.64861 55.17186 44.72625 35.23687 103.5073
R-Squared 0.82176 0.87177 0.88422 0.88902 0.90016 0.89231 0.85421 0.93869 0.92853 0.96116 0.94296 0.91053 0.87841
33
FIGURES
Figure 1: FORTS Instruments Trading Volume
Options; 5%*
Futures; 95%
* as of September 2, 2011
Figure 2: Hedging Coefficient Determination in Various Hedge Strategies
A
B *
C
D
Source: (Cecchetti, S. G., Cumby, R. E. and Figlewski, S. (1988). Estimation of the Optimal Futures Hedge. Review of Economics and Statistics, 7, 624)
Figure 3: RTS Index Futures Trading Volume, contracts
3 001 000
2 501 000
2 001 000
1 501 000
1 001 000
501 000
1 000
Figure 4: RTSI Futures Returns Beta Coefficient
0,95
0,90
0,85
0,80
0,75
0,70
0,65
0,60
35
Figure 5: RTS Index and RTS Index Futures Returns Volatility
0,09
0,08
0,07
0,06
0,05 RTSI Volatility 0,04 RTSIF Volatility 0,03
0,02
0,01
0
36
APPENDIX
RUSSIAN TRADING SYSTEM MATERIALS. INSTRUMENTS AND TECHNIQUES OF THE RTS DERIVATIVES MARKET.
Specifications of the Futures Contract on the RTS Index
Contract’s volume 2.0 USD x RTS Index value The Contract price In basic points of the RTS Index (Index value x 100) The value of one basic point of the RTS Index (lot) 0.02 USD
The minimum movement of the 5 basic points (0.1 USD at the USD/RUB exchange rate) Contract price (the tick) The Contract’s settlement methods Financial arrangements The Contract’s settlement months March, June, September, December The 15th day of the settlement month of the settlement year. If the 15th day is not a Trading day, then the contract’s The Contract’s last trading day last trading day shall be the Trading day, following the 15th day of the Contract’s settlement month of the settlement year. Spread date One following the nearest futures settlement date Minimum basic sizes of the initial 7.5% from the value of the contract margin Trading Time 10:30 – 23:50 (Moscow time) RTS-
RTS Index Futures: Possible Strategies
Strategy Type Description Hedging Stock Portfolio From Price To avoid losses associated with downward market movements, portfolio holders can sell futures contracts. As a Decrease result, losses on the spot market will be compensated by gains on FORTS. Using the RTS Index futures contracts, traders can hedge risks associated with the entire Russian securities market, not just stock-specific risks. Hedging Cash Inflow From Price Companies planning to invest in the stock market can hedge their risks arising from potential upward market Increase movement by buying RTS Index futures. Playing the Entire Securities Market RTS Index futures can be attractive in terms of playing the entire securities market moves, not just single stocks Moves price changes. Long or short positions in futures will depend on market situation in general. Leverage size for RTS Index futures operations equals 1:10 (the minimal size of a guarantee payment for each open position equals 10% of the contract value). Buying/Selling of Stock Portfolios That Buying an RTS Index futures contract is similar to creating a portfolio of 50 stocks – RTS Index constituents. Match the RTS Index Structure Transaction fees in this case will be significantly lower than those on the spot market because of lower tariffs and absence of depositary and leverage fees. RTS Index futures give market participants an opportunity to sell a portfolio identical in structure to the RTS Index. Management of Portfolios That Differ RTS Index futures allow participants to hedge risks not only for portfolios identical in composition to the RTS in Composition From the RTS Index Index constituents. Traders can hedge whole market risks of any portfolio using beta coefficients. Creating Synthetic Futures on the Opening opposite positions in RTS Index futures and futures on the Russian blue chips allows creating synthetic "Second-Tier Stock Index" futures on the “second-tier stock index”. Such operations allow to hedge portfolios consisting of second-tier stocks. Furthermore these synthetic futures are an attractive alternative to buying/selling second-tier stock portfolios on the spot market. Calendar Spread Holding a number of futures contracts with different settlement periods allows participants to take advantage of price spreads between the contracts. Futures and Options Combinations By combining RTS Index futures, different single stock futures and options, as well as the underlying assets traders can create complicated speculative and arbitrage strategies with different ”risk/profitability” ratios.
38